Jump to Content

A clinically applicable approach to continuous prediction of future acute kidney injury

Nenad Tomašev
Xavier Glorot
Jack W Rae
Michal Zielinski
Harry Askham
Andre Saraiva
Anne Mottram
Clemens Meyer
Suman Ravuri
Alistair Connell
Cían O Hughes
Julien Cornebise
Hugh Montgomery
Geraint Rees
Chris Laing
Clifton R Baker
Kelly Peterson
Ruth Reeves
Demis Hassabis
Dominic King
Mustafa Suleyman
Trevor Back
Christopher Nielson
Shakir Mohamed
Nature, vol. 572 (2019), pp. 116-119


The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients. To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records and using acute kidney injury—a common and potentially life-threatening condition—as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests. Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment.